Stage-Specific Pipeline View Using Prediction Engine

ABSTRACT

Techniques for displaying a stage-specific pipeline view with a prediction engine are disclosed. A system displays a plurality of regions representing various stages of completion for a plurality of transactions. The system determines a stage of completion for each of the plurality of transactions at a first point-in-time, and generates and displays visualizations representing each of the plurality of transactions in one of the plurality of regions based on the respective current stage of completion. Generating a visualization representing a first transaction includes determining a likelihood of the first transaction completing a stage associated with the first transaction. The likelihood may be determined by selecting attributes associated with the first transaction and identifying prior transactions with similar attributes. The system may compute the likelihood of the first transaction completing the stage based on completion rates associated with the prior transactions, and select the visualization based on the computed likelihood.

INCORPORATION BY REFERENCE; DISCLAIMER

Each of the following applications are hereby incorporated by reference:application Ser. No. 17/017,567 filed on Sep. 10, 2020; application No.62/900,504 filed on Sep. 14, 2019. The Applicant hereby rescinds anydisclaimer of claim scope in the parent application or the prosecutionhistory thereof and advises the USPTO that the claims in thisapplication may be broader than any claim in the parent application.

TECHNICAL FIELD

The present disclosure relates to dashboards. In particular, the presentdisclosure relates to dashboards with a pipeline view and predictionengine.

BACKGROUND

A dashboard is a type of graphical user interface which providesat-a-glance views of key performance indicators relevant to a particularobjective or business process. The dashboard is often displayed on a webpage which is linked to a data source that allows the report to beregularly updated. Dashboards may be laid out to track the flowsinherent in the business processes that they monitor. Specializeddashboards may track various corporate functions, including humanresources, recruiting, sales, operations, security, informationtechnology, project management, and customer relationship management.

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not by way oflimitation in the figures of the accompanying drawings. It should benoted that references to “an” or “one” embodiment in this disclosure arenot necessarily to the same embodiment, and they mean at least one. Inthe drawings:

FIG. 1 illustrates a pipeline visualization system in accordance withone or more embodiments;

FIG. 2 illustrates an example set of operations for displaying astage-specific pipeline view using a prediction engine in accordancewith one or more embodiments;

FIGS. 3A-3C show examples of a dashboard including a stage-specificpipeline view using a prediction engine in accordance with one or moreembodiments; and

FIG. 4 shows a block diagram that illustrates a computer system inaccordance with one or more embodiments.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding. One or more embodiments may be practiced without thesespecific details. Features described in one embodiment may be combinedwith features described in a different embodiment. In some examples,well-known structures and devices are described with reference to ablock diagram form in order to avoid unnecessarily obscuring the presentinvention.

-   -   1. GENERAL OVERVIEW    -   2. STAGE SPECIFIC PIPELINE VIEW ARCHITECTURE    -   3. DISPLAYING A STAGE-SPECIFIC PIPELINE VIEW USING A PREDICTION        ENGINE    -   4. MISCELLANEOUS; EXTENSIONS    -   5. HARDWARE OVERVIEW

1. GENERAL OVERVIEW

One or more embodiments include techniques for displaying astage-specific pipeline view using a prediction engine are disclosed. Asystem may display a plurality of regions a 3 respectively representingvarious stages of completion for a plurality of transactions at a firstpoint-in-time. The system may determine a stage of completion for eachof the plurality of transactions at the first point-in-time, and maygenerate and display visualizations representing each of the pluralityof transactions in one of the plurality of regions based on therespective current stage of completion.

Generating a visualization for representing a first transaction, of theplurality of transactions, includes determining a likelihood of thefirst transaction, of the plurality of transactions, completing a firststage currently associated with the first transaction. The likelihoodmay be determined by computing a first set of attributes associated withthe first transaction and identifying a plurality of prior transactionswith a corresponding set of attributes, when the prior transactions wereat the first stage, that meet a similarity criterion in relation to thefirst set of attributes associated with the first transaction. Thesystem may determine completion rates, associated with the plurality ofprior transactions, for completion of the first stage, compute thelikelihood of the first transaction completing the first stage based onthe completion rates associated with the plurality of priortransactions, and select the visualization, for representing the firsttransaction in a representation of the first stage, based on thelikelihood of the first transaction completing the first stage.

The system may use machine learning to determine attributes of the firsttransaction, including a likelihood that the first transaction willcomplete a pipeline stage with which the transaction has beenassociated. The system may determine one or more similarity criteria forcomparing the first transaction to a plurality of historicaltransactions. The system may compare the first transaction to historicaldata of a plurality of identified past transactions based on one or moretransaction attributes. A likelihood of the particular transactioncompleting the stage of the pipeline in the specified time period can bedetermined based on a percentage of historical transactions, havingsimilar attributes, which completed the pipeline stage within thespecified time period.

One or more embodiments described in this Specification and/or recitedin the claims may not be included in this General Overview section.

2. STAGE SPECIFIC PIPELINE VIEW ARCHITECTURE

FIG. 1 illustrates a system 100 in accordance with one or moreembodiments. As illustrated in FIG. 1 , system 100 includes a pipelinevisualization engine 102, a user interface 116, an external data source120, and various components thereof. In one or more embodiments, thesystem 100 may include more or fewer components than the componentsillustrated in FIG. 1 . The components illustrated in FIG. 1 may belocal to or remote from each other. The components illustrated in FIG. 1may be implemented in software and/or hardware. Each component may bedistributed over multiple applications and/or machines. Multiplecomponents may be combined into one application and/or machine.Operations described with respect to one component may instead beperformed by another component.

In one or more embodiments, the user interface 116 refers to hardwareand/or software configured to facilitate communications between a userand the search result association engine 102. The user interface 116 maybe used by a user who accesses an interface (e.g., a dashboardinterface) for work and/or personal activities. The user interface 116may be associated with one or more devices for presenting visual media,such as a display 118, including a monitor, a television, a projector,and/or the like. User interface 116 renders user interface elements andreceives input via user interface elements. Examples of interfacesinclude a graphical user interface (GUI), a command line interface(CLI), a haptic interface, and a voice command interface. Examples ofuser interface elements include checkboxes, radio buttons, dropdownlists, list boxes, buttons, toggles, text fields, date and timeselectors, command lines, sliders, pages, and forms.

In an embodiment, different components of the user interface 116 arespecified in different languages. The behavior of user interfaceelements is specified in a dynamic programming language, such asJavaScript. The content of user interface elements is specified in amarkup language, such as hypertext markup language (HTML) or XML UserInterface Language (XUL). The layout of user interface elements isspecified in a style sheet language, such as Cascading Style Sheets(CSS). Alternatively, the user interface 116 is specified in one or moreother languages, such as Java, C, or C++.

In one or more embodiments, a pipeline visualization engine 102 refersto hardware and/or software configured to perform operations describedherein for displaying a pipeline representing transaction completionstages to a user. Examples of operations for displaying the pipeline toa user are described below with reference to FIG. 2 .

In an embodiment, the pipeline visualization engine 102 includes atransaction gathering component 104. A transaction gathering component104 may refer to hardware and/or software configured to performoperations described herein (including such operations as may beincorporated by reference) for retrieving one or more transactions foranalysis by the pipeline visualization engine 102.

In an embodiment, the pipeline visualization engine 102 includes a stagedetermining component 106. A stage determining component 106 may referto hardware and/or software configured to determine a stage of aparticular transaction from among the gathered transactions.

In an embodiment, the pipeline visualization engine 102 includes acompletion likelihood determination component 108. A completionlikelihood determination component 108 may refer to hardware and/orsoftware configured to determine a likelihood that a particulartransaction from among the gathered transactions will complete thedetermined stage within a particular amount of time.

In an embodiment, the search result association engine 102 includes avisualization generation component 110. A visualization generationcomponent 110 may refer to hardware and/or software configured togenerate a visualization of the particular transaction based at least inpart on the stage associated with the transaction and the likelihoodthat the particular transaction will complete the particular stage in aparticular amount of time.

In an embodiment, one or more components of the search resultassociation engine 102 use a machine learning engine 112. Machinelearning includes various techniques in the field of artificialintelligence that deal with computer-implemented, user-independentprocesses for solving problems that have variable inputs.

In some embodiments, the machine learning engine 112 trains a machinelearning model 114 to perform one or more operations. Training a machinelearning model 114 uses training data to generate a function that, givenone or more inputs to the machine learning model 114, computes acorresponding output. The output may correspond to a prediction based onprior machine learning. In an embodiment, the output includes a label,classification, and/or categorization assigned to the provided input(s).The machine learning model 114 corresponds to a learned model forperforming the desired operation(s) (e.g., labeling, classifying, and/orcategorizing inputs). For example, the machine learning model 112 may beused in determining a likelihood of a transaction to complete a stage inparticular amount of time.

In an embodiment, the machine learning engine 112 may use supervisedlearning, semi-supervised learning, unsupervised learning, reinforcementlearning, and/or another training method or combination thereof. Insupervised learning, labeled training data includes input/output pairsin which each input is labeled with a desired output (e.g., a label,classification, and/or categorization), also referred to as asupervisory signal. In semi-supervised learning, some inputs areassociated with supervisory signals and other inputs are not associatedwith supervisory signals. In unsupervised learning, the training datadoes not include supervisory signals. Reinforcement learning uses afeedback system in which the machine learning engine 108 receivespositive and/or negative reinforcement in the process of attempting tosolve a particular problem (e.g., to optimize performance in aparticular scenario, according to one or more predefined performancecriteria). In an embodiment, the machine learning engine 112 initiallyuses supervised learning to train the machine learning model 114 andthen uses unsupervised learning to update the machine learning model 114on an ongoing basis.

In an embodiment, a machine learning engine 112 may use many differenttechniques to label, classify, and/or categorize inputs. A machinelearning engine 112 may transform inputs into feature vectors thatdescribe one or more properties (“features”) of the inputs. The machinelearning engine 112 may label, classify, and/or categorize the inputsbased on the feature vectors. Alternatively or additionally, a machinelearning engine 112 may use clustering (also referred to as clusteranalysis) to identify commonalities in the inputs. The machine learningengine 112 may group (i.e., cluster) the inputs based on thosecommonalities. The machine learning engine 112 may use hierarchicalclustering, k-means clustering, and/or another clustering method orcombination thereof. In an embodiment, a machine learning engine 112includes an artificial neural network. An artificial neural networkincludes multiple nodes (also referred to as artificial neurons) andedges between nodes. Edges may be associated with corresponding weightsthat represent the strengths of connections between nodes, which themachine learning engine 112 adjusts as machine learning proceeds.Alternatively or additionally, a machine learning engine 112 may includea support vector machine. A support vector machine represents inputs asvectors. The machine learning engine 112 may label, classify, and/orcategorizes inputs based on the vectors. Alternatively or additionally,the machine learning engine 112 may use a naïve Bayes classifier tolabel, classify, and/or categorize inputs. Alternatively oradditionally, given a particular input, a machine learning model mayapply a decision tree to predict an output for the given input.Alternatively or additionally, a machine learning engine 112 may applyfuzzy logic in situations where labeling, classifying, and/orcategorizing an input among a fixed set of mutually exclusive options isimpossible or impractical. The aforementioned machine learning model 114and techniques are discussed for exemplary purposes only and should notbe construed as limiting one or more embodiments.

In an embodiment, as a machine learning engine 112 applies differentinputs to a machine learning model 114, the corresponding outputs arenot always accurate. As an example, the machine learning engine 112 mayuse supervised learning to train a machine learning model 114. Aftertraining the machine learning model 114, if a subsequent input isidentical to an input that was included in labeled training data and theoutput is identical to the supervisory signal in the training data, thenoutput is certain to be accurate. If an input is different from inputsthat were included in labeled training data, then the machine learningengine 112 may generate a corresponding output that is inaccurate or ofuncertain accuracy. In addition to producing a particular output for agiven input, the machine learning engine 112 may be configured toproduce an indicator representing a confidence (or lack thereof) in theaccuracy of the output. A confidence indicator may include a numericscore, a Boolean value, and/or any other kind of indicator thatcorresponds to a confidence (or lack thereof) in the accuracy of theoutput.

In an embodiment, the pipeline visualization engine 102 is configured toreceive data from one or more external data sources 120. An externaldata source 120 refers to hardware and/or software operating independentof the search result association engine 102. For example, the hardwareand/or software of the external data source 120 may be under control ofa different entity (e.g., a different company or other kind oforganization) than an entity that controls the query suggestion engine.An external data source 120 may store transaction data associated withone or more currently pending transactions and/or one or more potentialtransactions. An example of an external data source 120 supplying datato a pipeline visualization engine 102 may include a third partydatabase. Many different kinds of external data sources 120 may supplymany different kinds of data.

In an embodiment, pipeline visualization engine 102 is configured toretrieve data from an external data source 120 by ‘pulling’ the data viaan application programming interface (API) of the external data source120, using user credentials that a user has provided for that particularexternal data source 120. Alternatively or additionally, an externaldata source 120 may be configured to ‘push’ data to the pipelinevisualization engine 102 via an API of the query suggestion service,using an access key, password, and/or other kind of credential that auser has supplied to the external data source 120. A pipelinevisualization engine 102 may be configured to receive data from anexternal data source 120 in many different ways.

In an embodiment, the system 100 is implemented on one or more digitaldevices. The term “digital device” generally refers to any hardwaredevice that includes a processor. A digital device may refer to aphysical device executing an application or a virtual machine. Examplesof digital devices include a computer, a tablet, a laptop, a desktop, anetbook, a server, a web server, a network policy server, a proxyserver, a generic machine, a function-specific hardware device, ahardware router, a hardware switch, a hardware firewall, a hardwarefirewall, a hardware network address translator (NAT), a hardware loadbalancer, a mainframe, a television, a content receiver, a set-top box,a printer, a mobile handset, a smartphone, a personal digital assistant(“PDA”), a wireless receiver and/or transmitter, a base station, acommunication management device, a router, a switch, a controller, anaccess point, and/or a client device.

3. DISPLAYING A STAGE-SPECIFIC PIPELINE VIEW USING A PREDICTION ENGINE

FIG. 2 illustrates an example set of operations for displaying astage-specific pipeline view with a prediction engine in accordance withone or more embodiments. One or more operations illustrated in FIG. 2may be modified, rearranged, or omitted all together. Accordingly, theparticular sequence of operations illustrated in FIG. 2 should not beconstrued as limiting the scope of one or more embodiments.

In embodiments, a system, such as a dashboard display system, maydisplay a dashboard including a pipeline visualizer for displayingprojects (such as sales transactions) in multiple stages of completion(Operation 202). For example, the system may include a display deviceconfigured to display the dashboard to a user. In some embodiments, thepipeline may be divided into multiple stages horizontally (e.g., alongthe x-axis). For example, as shown in FIG. 3A, the pipeline can comprisefour segments for displaying sales transactions, including “Leads,”“Opportunities,” “Proposals,” and “Won.” In some aspects, the pipelinevisualization may include a particular pipeline stage and a plurality ofsubstages associated with the particular pipeline stage.

The pipeline display may also include a timeline illustrating at least aparticular point-in-time. The particular point in time may be thepresent time, a point-in-time in the past (e.g., before the presenttime), or a point0in-time in the future (e.g., after the present time).In embodiments, the user may adjust a marker on the timeline to select apoint-in-time as the particular point-in-time.

In embodiments, the system may receive, as input, a plurality oftransactions. In some embodiments, the plurality of transactions isreceived in response to a user request for transactions. Each of thetransactions received as input may be associated with the user (e.g.,the user has performed some amount of work associated with thetransaction and/or the transaction has been assigned to the user).Additionally or alternatively, the system may include one or moretransactions that is not associated with the user. For example, thesystem may include one or more transactions not associated with any user(e.g., a transaction that no user has performed work on and/or that hasnot been assigned to any user). In some embodiments, the received inputmay include one or more transaction attributes (e.g., transactionsassociated with a particular pipeline stage, transactions having aparticular dollar value).

Each transaction may include data specifying one or more characteristicsof the transaction. For example, each transaction may include dataspecifying a transaction dollar amount, a party associated with thetransaction, and/or a contact associated with the transaction. Inembodiments, the transaction may include data indicating a most recentcommunication with the contact and/or data indicating a frequency ofcommunication with the contact.

In some embodiments, the plurality of transactions may be received fromone or more external data sources. The system may be configured toretrieve at least a subset of the plurality of transactions from anexternal data source by ‘pulling’ the transaction data via anapplication programming interface (API) of the external data source,using user credentials that a user has provided for that particularexternal data source. Alternatively or additionally, an external datasource may be configured to ‘push’ at least a subset of the plurality oftransactions to the system. The transaction data may be pushed to thesystem via an API of the system, using an access key, password, and/orother kind of credential that a user has supplied to the external datasource.

In some embodiments, the pipeline visualization may include additionalinformation. The additional information may include aggregateinformation associated with the plurality of transactions and/oruser-associated information. For example, as shown in FIG. 3A, thepipeline visualization includes aggregate information of an expectedrevenue figure and user-associated information including a sales goal of$100,00 and a number of days left in the current sales quarter.

The system may determine a stage of completion for a particulartransaction of the plurality of transactions at the particularpoint-in-time (Operation 204). In some embodiments, the particularpoint-in-time may be the present time or a point in time prior to thepresent time. In embodiments, the particular transaction can beassociated with a stage of the pipeline. For example, as shown in FIG.3A, the particular transaction can be associated with a particular stageof the pipeline (e.g., one of “Leads,” “Opportunities,” “Proposals,” or“Won.”). For example, the particular transaction can include metadataindicating completeness of the transaction.

In embodiments, the particular point-in-time may be in the future. Thedetermined stage of completion for a point-in-time in the future may bedetermined by one or more of a conversion rate associated with theparticular user, a conversion rate associated with a company at whichthe particular user works, and/or a conversion rate associated withdeals having a dollar value similar to the dollar value of theparticular deal. Many factors may be used when determining theconversion rate.

The system may determine a likelihood that the particular transactionwill complete the stage of the pipeline with which the particulartransaction has been associated In particular, the system may specify atime period, and may determine a likelihood that the particulartransaction will complete the stage of the pipeline in the specifiedtime period. In embodiments, machine learning can be used to determine alikelihood that the particular transaction will complete the stage ofthe pipeline in the specified time period. For example, the system maycompare the particular transaction to historical data of a plurality ofpast transactions based on one or more transaction attributes. Alikelihood of the particular transaction completing the stage of thepipeline in the specified time period can be determined based on apercentage of historical transactions, having similar attributes, whichcompleted the pipeline stage within the specified time period.

As part of generating a visualization, the system may determine a firstset of associated with the particular transaction (Operation 206). Forexample, the system may read or otherwise decode the attributes from adata source. In some embodiments, the attributes can be stored togetherwith the associated transactions. The attributes may include, forexample, one or more of a dollar value associated with the transaction,a client associated with the transaction, an indicator of how recentlythe client was contacted, or an indicator of how frequently the clientis contacted. Many different types of data may be maintained in thefirst set of attributes.

In embodiments, the system may identify one or more prior transactionshaving similar attributes to those of the selected transaction(Operation 208). The system may identify attributes of the priortransaction based on the attributes of the prior transactions at thetime the transaction entered the stage of the pipeline corresponding tothe stage of the pipeline associated with the particular transaction(e.g., if the particular transaction is associated with the “Proposals”stage, the system may determine attributes of the prior transactions atthe time the prior transactions entered the “Proposals” stage).Identifying the one or more prior transactions may include determiningone or more similarity criteria for identifying the prior transactions.Many different kinds of similarity criteria may be defined. In anembodiment, a system may use a machine learning engine to determinesimilarity criteria as part of a machine learning model in a machinelearning engine.

In an embodiment, a machine learning engine may use many differenttechniques to label, classify, and/or categorize the particulartransaction and the one or more historical transactions as inputs. Amachine learning engine may transform the inputs into feature vectorsthat describe one or more properties (“features”) of the inputs. Themachine learning engine may label, classify, and/or categorize theinputs based on the feature vectors. Alternatively or additionally, amachine learning engine may use clustering (also referred to as clusteranalysis) to identify commonalities in the inputs. The machine learningengine may group (i.e., cluster) the inputs based on thosecommonalities. The machine learning engine may use hierarchicalclustering, k-means clustering, and/or another clustering method orcombination thereof. In an embodiment, a machine learning engineincludes an artificial neural network. An artificial neural networkincludes multiple nodes (also referred to as artificial neurons) andedges between nodes. Edges may be associated with corresponding weightsthat represent the strengths of connections between nodes, which themachine learning engine adjusts as machine learning proceeds.Alternatively or additionally, a machine learning engine may include asupport vector machine. A support vector machine represents inputs asvectors. The machine learning engine may label, classify, and/orcategorizes inputs based on the vectors. Alternatively or additionally,the machine learning engine may use a naïve Bayes classifier to label,classify, and/or categorize inputs. Alternatively or additionally, givena particular input, a machine learning model may apply a decision treeto predict an output for the given input. Alternatively or additionally,a machine learning engine may apply fuzzy logic in situations wherelabeling, classifying, and/or categorizing an input among a fixed set ofmutually exclusive options is impossible or impractical. Theaforementioned machine learning model and techniques are discussed forexemplary purposes only and should not be construed as limiting one ormore embodiments.

The system may further determine completion rates of the identifiedprior transactions (Operation 210). In some embodiments, the completionrate of each prior transaction can be stored as a portion of theattributes of the prior transaction. As an example, for a pipeline thatincludes four stages, the attributes of the prior transactions mayinclude a length of time to complete the first stage, a length of timeto complete the second stage, a length of time to complete the thirdstage, and a length of time to complete the fourth stage.

In embodiments, the system may compute a likelihood of the firsttransaction completing the first stage (Operation 212). In someembodiments, the likelihood is computed based on the completion ratesassociated with eh one or more prior transactions. For example, in someembodiments, the completion rate may be computed as an average of thecompletion rates of the prior transactions. As another example, thelikelihood of the first transaction completing the first stage can becomputed as a weighted average of the completion rates of the priortransactions, where weights are assigned to the prior transactions basedon similarity to the first transaction.

In embodiments a visualization can be selected for the particulartransaction (Operation 214). The visualization may include an icon thatrepresents the transaction visually, wherein aspects of the icon can bevaried based on attributes of the particular transaction. For example,an icon size, an icon color, and/or an icon outline can be variedaccording to one or more attributes of the particular transaction. Insome embodiments, characteristics of the visualization can be selectedat least in part, based on the likelihood of the first transactioncompleting the first stage, as computed in Operation 212. For example,one or more of a size of the visualization and a color of thevisualization can be selected based on the computed likelihood. Inembodiments, additional attributes of the particular transaction, suchas transaction dollar amount, the frequency of communication with thecompany associated with the transaction, and/or the freshness of thetransaction can be considered when determining characteristics of thevisualization. As a particular example, a color (e.g., a hue, shade, ortint) and/or pattern associated with the visualization of the particulartransaction may be selected based on the computed likelihood that theparticular transaction will complete the associated pipeline stage, anda size associated with the visualization may be selected based on adollar value associated with the particular transaction. In someembodiments, a line style associated with the visualization can furtherbe selected as part of selecting the visualization. For example,transactions that have not yet been realized may use a dashed linestyle, while realized transactions use a solid line style. As anexample, FIG. 3B shows a pipeline view including visualizations oftransactions that have not yet been realized using a dashed line style,while realized transactions use a solid line style. Additionally, FIG.3B shows visualizations of realized transactions are shown havingdifferent colors corresponding to likelihood that the transaction willcomplete the current pipeline stage, and different sizes based on thetransaction amount. Many different visualization characteristics can bedetermined based on attributes of the first transaction.

Finally, the visualized transaction can be displayed on the pipelineview (Operation 216). As an example, the visualized transaction can bedisplayed concurrently with the pipeline view, such that the visualizedtransaction appears to be in the pipeline.

In embodiments of the invention, this process can be repeated multipletimes for different transactions. For example, the process can berepeated until all transactions received as input have been visualized.As an example, FIG. 3B shows a pipeline view that includes numeroustransaction visualizations displayed concurrently.

Additionally, the system may receive input from a user indicating adifferent point-in-time. In some embodiments, the input can comprise auser interacting with the timeline to select a point along the timeline.For example, the user may click and drag a time marker on the timelineto the different point-in-time.

In some embodiments the different point in time can be prior to thefirst point-in-time. The system may refresh the display of the pipelineview and the visualizations of the transactions based on the differentpoint-in-time.

In some embodiments, the different point in time can be subsequent tothe first point in time and indicate a time that has not yet occurred(e.g., it is a point-in-time in the future). The system may estimate astage of completion for each transaction at the different point in time.For example, the system may use a machine learning model to estimate thestage of completion for each of the respective transactions. The systemmay further refresh the display of the pipeline and the visualizationsof the transactions based on the estimated stage of completion for eachof the transactions.

In some embodiments, a user can select a displayed visualization of atransaction. In response to the user making such a selection, the systemcan display information related to the transaction. As a specificexample, FIG. 3C shows transactions details related to a selectedtransaction visualization. In embodiments. the visualized transactionsmay be positioned randomly within their assigned pipeline stages, asshown in FIG. 3C. Alternatively, the transactions assigned to aparticular pipeline stage may be sorted based on one or more transactionattributes.

4. MISCELLANEOUS; EXTENSIONS

Embodiments are directed to a system with one or more devices thatinclude a hardware processor and that are configured to perform any ofthe operations described herein and/or recited in any of the claimsbelow.

In an embodiment, a non-transitory computer readable storage mediumcomprises instructions which, when executed by one or more hardwareprocessors, causes performance of any of the operations described hereinand/or recited in any of the claims.

Any combination of the features and functionalities described herein maybe used in accordance with one or more embodiments. In the foregoingspecification, embodiments have been described with reference tonumerous specific details that may vary from implementation toimplementation. The specification and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the invention, and what isintended by the applicants to be the scope of the invention, is theliteral and equivalent scope of the set of claims that issue from thisapplication, in the specific form in which such claims issue, includingany subsequent correction.

5. HARDWARE OVERVIEW

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), or network processing units (NPUs)that are persistently programmed to perform the techniques, or mayinclude one or more general purpose hardware processors programmed toperform the techniques pursuant to program instructions in firmware,memory, other storage, or a combination. Such special-purpose computingdevices may also combine custom hard-wired logic, ASICs, FPGAs, or NPUswith custom programming to accomplish the techniques. Thespecial-purpose computing devices may be desktop computer systems,portable computer systems, handheld devices, networking devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk or optical disk, is provided and coupled to bus402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 410.Volatile media includes dynamic memory, such as main memory 406. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge,content-addressable memory (CAM), and ternary content-addressable memory(TCAM).

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk or solidstate drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. The specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense. The sole and exclusive indicator of the scope of the invention,and what is intended by the applicants to be the scope of the invention,is the literal and equivalent scope of the set of claims that issue fromthis application, in the specific form in which such claims issue,including any subsequent correction.

What is claimed is:
 1. One or more non-transitory media comprisinginstructions which, when executed by one or more hardware processors,cause performance of operations comprising: displaying a plurality ofregions respectively representing a plurality of stages for a pluralityof transactions; for each particular transaction of the plurality oftransactions: determining a first respective stage of the particulartransaction at a first point-in-time; determining a respectivelikelihood of the particular transaction completing the first respectivestage; displaying a first visualization representing the particulartransaction in a first respective region of the plurality of regionsthat corresponds to the first respective stage, based at least in parton the respective likelihood of the particular transaction completingthe first respective stage; determining a second respective stage of theparticular transaction at a second point-in-time; displaying a secondvisualization representing the particular transaction in a secondrespective region of the plurality of regions that corresponds to thesecond respective stage.
 2. The one or more media of claim 1, whereindetermining the respective likelihood of the particular transactioncompleting the first respective stage comprises: identifying a subset ofthe plurality of prior transactions such that (a) one or more attributesassociated with the particular transaction satisfy one or moresimilarity criteria with respect to (b) one or more correspondingattributes associated the subset of the plurality of transactions whenthe subset of the plurality of transactions were at the first respectivestage; computing the respective likelihood of the particular transactioncompleting the first respective stage, based at least in part on acompletion rate associated with the subset of the plurality of priortransactions completing the first respective stage.
 3. The one or moremedia of claim 2, wherein machine learning is used to select the one ormore similarity criteria.
 4. The one or more media of claim 2, whereinmachine learning is used to identify the subset of the plurality ofprior transactions.
 5. The one or more media of claim 1, the operationsfurther comprising: determining, based at least in part on therespective likelihood of the particular transaction completing the firstrespective stage, one or more of: a size of the first visualization or acolor of the first visualization.
 6. The one or more media of claim 1,the operations further comprising: determining, based at least in parton an estimated financial reward for completing the particulartransaction, one or more of: a size of the first visualization or acolor of the first visualization.
 7. The one or more media of claim 1,wherein the respective likelihood of the particular transactioncompleting the first respective stage is determined based at least inpart on a recency of contact with a customer associated with theparticular transaction.
 8. A system comprising: one or more hardwareprocessors; one or more non-transitory computer-readable media; andprogram instructions stored on the one or more non-transitory computerreadable media which, when executed by the one or more hardwareprocessors, cause the system to perform operations comprising:displaying a plurality of regions respectively representing a pluralityof stages for a plurality of transactions; for each particulartransaction of the plurality of transactions: determining a firstrespective stage of the particular transaction at a first point-in-time;determining a respective likelihood of the particular transactioncompleting the first respective stage; displaying a first visualizationrepresenting the particular transaction in a first respective region ofthe plurality of regions that corresponds to the first respective stage,based at least in part on the respective likelihood of the particulartransaction completing the first respective stage; determining a secondrespective stage of the particular transaction at a secondpoint-in-time; displaying a second visualization representing theparticular transaction in a second respective region of the plurality ofregions that corresponds to the second respective stage.
 9. The systemof claim 8, wherein determining the respective likelihood of theparticular transaction completing the first respective stage comprises:identifying a subset of the plurality of prior transactions such that(a) one or more attributes associated with the particular transactionsatisfy one or more similarity criteria with respect to (b) one or morecorresponding attributes associated the subset of the plurality oftransactions when the subset of the plurality of transactions were atthe first respective stage; computing the respective likelihood of theparticular transaction completing the first respective stage, based atleast in part on a completion rate associated with the subset of theplurality of prior transactions completing the first respective stage.10. The system of claim 9, wherein machine learning is used to selectthe one or more similarity criteria.
 11. The system of claim 9, whereinmachine learning is used to identify the subset of the plurality ofprior transactions.
 12. The system of claim 8, the operations furthercomprising: determining, based at least in part on the respectivelikelihood of the particular transaction completing the first respectivestage, one or more of: a size of the first visualization or a color ofthe first visualization.
 13. The system of claim 8, the operationsfurther comprising: determining, based at least in part on an estimatedfinancial reward for completing the particular transaction, one or moreof: a size of the first visualization or a color of the firstvisualization.
 14. The system of claim 8, wherein the respectivelikelihood of the particular transaction completing the first respectivestage is determined based at least in part on a recency of contact witha customer associated with the particular transaction.
 15. A methodcomprising: displaying a plurality of regions respectively representinga plurality of stages for a plurality of transactions; for eachparticular transaction of the plurality of transactions: determining afirst respective stage of the particular transaction at a firstpoint-in-time; determining a respective likelihood of the particulartransaction completing the first respective stage; displaying a firstvisualization representing the particular transaction in a firstrespective region of the plurality of regions that corresponds to thefirst respective stage, based at least in part on the respectivelikelihood of the particular transaction completing the first respectivestage; determining a second respective stage of the particulartransaction at a second point-in-time; displaying a second visualizationrepresenting the particular transaction in a second respective region ofthe plurality of regions that corresponds to the second respectivestage; wherein the method is performed by at least one device includinga hardware processor.
 16. The method of claim 15, wherein determiningthe respective likelihood of the particular transaction completing thefirst respective stage comprises: identifying a subset of the pluralityof prior transactions such that (a) one or more attributes associatedwith the particular transaction satisfy one or more similarity criteriawith respect to (b) one or more corresponding attributes associated thesubset of the plurality of transactions when the subset of the pluralityof transactions were at the first respective stage; computing therespective likelihood of the particular transaction completing the firstrespective stage, based at least in part on a completion rate associatedwith the subset of the plurality of prior transactions completing thefirst respective stage.
 17. The method of claim 16, wherein machinelearning is used to select the one or more similarity criteria.
 18. Themethod of claim 16, wherein machine learning is used to identify thesubset of the plurality of prior transactions.
 19. The method of claim15, further comprising: determining, based at least in part on therespective likelihood of the particular transaction completing the firstrespective stage, one or more of: a size of the first visualization or acolor of the first visualization.
 20. The method of claim 15, furthercomprising: determining, based at least in part on an estimatedfinancial reward for completing the particular transaction, one or moreof: a size of the first visualization or a color of the firstvisualization.